Realistic models for cancer research representing disease progression that commensurately respond to therapeutics consistent with clinical observation are the holy grail for pre-clinical research and screening.Althoug...Realistic models for cancer research representing disease progression that commensurately respond to therapeutics consistent with clinical observation are the holy grail for pre-clinical research and screening.Although such an ideal is elusive,well-characterized in vivo models facilitate our understanding of disease,progression,and therapeutic opportunities.Here,we characterize a commonly used syngeneic BALB/c mouse model of triple negative breast cancer(4T1)after establishing tumors in their flanks.Tumors developed at the subcutaneous injection site for all experimental mice and their volumes were monitored.We quantified a rare subset of breast cancer stemlike cells(CSCs),classified as CD44^(+)/CD24^(−)phenotypes in in vitro and ex vivo cell populations.Chromosome numbers in ex vivo metaphase cells were greater than cells cultured in vitro(89.4±3.4,range of 70-132 and 82.6±1.1,range of 70-128;respectively).Further,we observed different types of chromosome aberrations,including gap,deletion,exchange,interstitial deletion,terminal deletion,ring,dicentric,and Robertsonian translocations.For both sources of cells,the number of aberrations was dominated by deletions,terminal deletions,and Robertsonian translocations.Ex vivo cells exhibited greater prevalence of deletions and terminal deletions,whereas in vitro cells displayed more ring aberrations and Robertsonian translocations.In conclusion,we successfully characterized cancer cells from a syngeneic mouse model of breast cancer in terms of rare CSC proportion and a variety of chromosomal aberrations,which is useful for understanding tumor traits associated with cancer development and therapeutic action.The data act as a valuable resource for other studies using the 4T1 BALB/c model.展开更多
Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and ...Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and finite element analyses.Three tests of the slope deformation under earthquake and post-rainfall earthquakes are first studied using image analysis techniques.Then,based on an elastoplastic constitutive model,numerical simulations are carried out using the finite element method and compared with the centrifuge test results.Finally,a parametric study is performed to clarify the effects of antecedent rainfall on earthquake-induced slope deformation.The results show that slope deformation caused by post-rainfall earthquakes differs from that caused by earthquakes without antecedent rainfall.The seepage flow and soil strength of the slope are affected by previous rainfall conditions,such as intensity and duration,which directly influence the slope deformation caused by the subsequent earthquake.Soil displacement and strain become greater and the slip surface is more noticeable during the post-rainfall earthquake of higher intensity.In addition,the time interval between the rainfall and the earthquake has a considerable impact on the detailed characteristics of the slope deformation,and the significant deformation occurs at the time of lowest soil strength when seepage flow reaches the lower part of the slope.Moreover,the repeated intermittent rainfall greatly affects the subsequent earthquake-induced slope deformation,the main characteristics of which are closely related to the changes in saturation and strength of the slope.However,with the prolonged time gap between each round of rainfall,the earthquake-induced slope deformation becomes insignificant.展开更多
Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been propo...Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.展开更多
Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that man...Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.展开更多
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita...BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.展开更多
Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys...Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.展开更多
Ion-exchange Polymer-Metal Composites(IPMCs)gain huge attentions due to large deformation,rapid electromechanical response,and high energy conversion efficiency.Deflection of IPMC arises from the volumetric swelling e...Ion-exchange Polymer-Metal Composites(IPMCs)gain huge attentions due to large deformation,rapid electromechanical response,and high energy conversion efficiency.Deflection of IPMC arises from the volumetric swelling effect induced by the concentration gradient of hydrated cations between the two electrodes,thus the volume of hydrated cation deter-mines the motion magnitude and direction of IPMC.H ion is one of the most commonly used driving cations for IPMC.However,due to its unique characteristics,particularly the inability to accurately quantify its hydration volume,existing literatures primarily focus on the physical driving models for metallic cations,i.e.,Na+,no driving model for the H ion is reported until now.This paper proposes a novel model of H ion escape from the water's body-centered cubic lattice to count the hydration volume.Number(n)of water molecules carried by the H ion is solved by combining the Lennard-Jones potential energy function with Maxwell's velocity distribution.The specific n value is equivalent to 4.04 for the H ion inside Nafion electrolyte under a 3.0 V DC electric field.Substituting it into the classic Friction Model(proposed by Tadokoro et al.at 2000),actuation behaviors of H ion driven IPMC were therefore achieved through Matlab calculations and Abaqus simulations.The calculated results of dynamic displacement and force highly match to the experimental data form the Nafion IPMC actuator driven by same electric field,showing a highly reliability of the established escape model.展开更多
The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir upli...The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir uplift.Seismic imaging revealed that the upper slab was scraped and that the lower slab had subducted to a depth of>150 km.These features constitute the tectonic complexity of the Pamirs,as well as the thermal subduction mechanism involved,which remains poorly understood.Hence,in this study,high-resolution three-dimensional(3D)kinematic modeling is applied to investigate the thermal structure and geometry of the subducting slab beneath the Pamirs.The modeled slab configuration reveals distinct along-strike variations,with a steeply dipping slab beneath the southern Pamirs,a more gently inclined slab beneath the northern Pamirs,and apparent upper slab termination at shallow depths beneath the Pamirs.The thermal field reveals a cold slab core after delamination,with temperatures ranging from 400℃to 800℃,enveloped by a hotter mantle reaching~1400℃.The occurrence of intermediate-depth earthquakes aligns primarily with colder slab regions,particularly near the slab tear-off below the southwestern Pamirs,indicating a strong correlation between slab temperature and seismicity.In contrast,the northern Pamirs exhibit reduced seismicity at depth,which is likely associated with thermal weakening and delamination.The central Pamirs show a significant thermal anomaly caused by a concave slab,where the coldest crust does not descend deeply,further suggesting crustal detachment or mechanical failure.The lateral asymmetry in slab temperature possibly explains the mechanism of lateral tearing and differential slab-mantle coupling.展开更多
In the lush heart of Uganda’s Busoga sub-region,Isaac Imaka is charting a new course for rural development.After seven years in national media,he left the newsroom and stepped into the soil.The former reporter with t...In the lush heart of Uganda’s Busoga sub-region,Isaac Imaka is charting a new course for rural development.After seven years in national media,he left the newsroom and stepped into the soil.The former reporter with the Daily Monitor was driven by the belief that communities like his in Jinja North deserved more than chronic poverty and hand-to-mouth survival.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
Fluid flow through fractured rock masses is a key process controlling the safety and performance of deep geoengineering systems,shaped by the complex interactions of thermal,hydraulic,mechanical and chemical(THMC)fiel...Fluid flow through fractured rock masses is a key process controlling the safety and performance of deep geoengineering systems,shaped by the complex interactions of thermal,hydraulic,mechanical and chemical(THMC)fields.This paper presents a systematic review of this subject with special emphasis on the multi-physics governing it.First,we elucidate the interdependent mechanisms and governing equations,highlighting the nonlinear,path-dependent,and evolving nature of the relationship between stress and permeability.Next,mainstream modeling approaches,including equivalent continuum,discrete fracture network(DFN),and dual-porosity/dual-permeability methods,are critically evaluated,and a strategy for model selection based on project scale and geological context is proposed accordingly.Moreover,experimental insights from single-fracture and triaxial flow studies are synthesized,revealing how effective stress,shear displacement,and fracture roughness control permeability evolution.In particular,the practical significance of THMC coupling is demonstrated through case studies on nuclear waste disposal,Enhanced Geothermal Systems,and tunneling projects.The reviewfurther explores AI-and machine learning-driven innovations,particularly physics-informed neural networks and hybrid modeling,which address limitations in computational efficiency,data scarcity,and physical consistency.Finally,persistent challenges,including multi-scale coupling,parameter uncertainty,and complex fracture network representation are identified and critically discussed while paying attention to future developments.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visite...Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visited our hospital from January 2024 to December 2024 were selected as samples and randomly divided into groups.The observation group received rehabilitation nursing based on the mind mapping model combined with psychological intervention,while the control group received routine intervention.The differences in emotional scores,self-care ability scores,compliance,and complications were compared between the two groups.Results:The anxiety(SAS)and depression(SDS)scores of the observation group were lower than those of the control group,while the self-care ability scale(ESCA)score was higher than that of the control group(P<0.05).The compliance rate of the observation group was higher than that of the control group(P<0.05).The complication rate of NS in the observation group was lower than that in the control group(P<0.05).Conclusion:Rehabilitation nursing based on the mind mapping model combined with psychological intervention can enhance self-care ability,reduce negative emotions,and reduce complications in NS nursing,which is efficient and feasible.展开更多
Background:Due to the widespread use of cell phone devices today,numerous re-search studies have focused on the adverse effects of electromagnetic radiation on human neuropsychological and reproductive systems.In most...Background:Due to the widespread use of cell phone devices today,numerous re-search studies have focused on the adverse effects of electromagnetic radiation on human neuropsychological and reproductive systems.In most studies,oxidative stress has been identified as the primary pathophysiological mechanism underlying the harmful effects of electromagnetic waves.This paper aims to provide a holistic review of the protective effects of melatonin against cell phone-induced electromag-netic waves on various organs.Methods:This study is a systematic review of articles chosen by searching Google Scholar,PubMed,Embase,Scopus,Web of Science,and Science Direct using the key-words‘melatonin’,‘cell phone radiation’,and‘animal model’.The search focused on articles written in English,which were reviewed and evaluated.The PRISMA process was used to review the articles chosen for the study,and the JBI checklist was used to check the quality of the reviewed articles.Results:In the final review of 11 valid quality-checked articles,the effects of me-latonin in the intervention group,the effects of electromagnetic waves in the case group,and the amount of melatonin in the chosen organ,i.e.brain,skin,eyes,testis and the kidney were thoroughly examined.The review showed that electromagnetic waves increase cellular anti-oxidative activity in different tissues such as the brain,the skin,the eyes,the testis,and the kidneys.Melatonin can considerably augment the anti-oxidative system of cells and protect tissues;these measurements were sig-nificantly increased in control groups.Electromagnetic waves can induce tissue atro-phy and cell death in various organs including the brain and the skin and this effect was highly decreased by melatonin.Conclusion:Our review confirms that melatonin effectively protects the organs of an-imal models against electromagnetic waves.In light of this conclusion and the current world-wide use of melatonin,future studies should advance to the stages of human clinical trials.We also recommend that more research in the field of melatonin physi-ology is conducted in order to protect exposed cells from dying and that melatonin should be considered as a pharmaceutical option for treating the complications result-ing from electromagnetic waves in humans.展开更多
Measurements from geomagnetic satellites continue to underpin advances in geomagnetic field models that describe Earth's internally generated magnetic field.Here,we present a new field model,MSCM,that integrates v...Measurements from geomagnetic satellites continue to underpin advances in geomagnetic field models that describe Earth's internally generated magnetic field.Here,we present a new field model,MSCM,that integrates vector and scalar data from the Swarm,China Seismo-Electromagnetic Satellite(CSES),and Macao Science Satellite-1(MSS-1)missions.The model spans from 2014.0 to 2024.5,incorporating the core,lithospheric,and magnetospheric fields,and it shows characteristics similar to other published models based on different data.For the first time,we demonstrate that it is possible to successfully construct a geomagnetic field model that incorporates CSES vector data,albeit one in which the radial and azimuthal CSES vector components are Huber downweighted.We further show that data from the MSS-1 can be integrated within an explicitly smoothed,fully time-dependent model description.Using the MSCM,we identify new behavior of the South Atlantic Anomaly,the broad region of low magnetic field intensity over the southern Atlantic.This prominent feature appears split into a western part and an eastern part,each with its own intensity minimum.Since 2015,the principal western minimum has undergone only modest intensity decreases of 290 nT and westward motion of 20 km per year,whereas the recently formed eastern minimum has shown a 2–3 times greater intensity drop of 730 nT with no apparent east-west motion.展开更多
Ice load on underwater vehicles breaking through ice covers from underneath is a significant concern for researchers in polar exploration,and the research on this problem is still in its early stages.Both mechanical e...Ice load on underwater vehicles breaking through ice covers from underneath is a significant concern for researchers in polar exploration,and the research on this problem is still in its early stages.Both mechanical experimental measurement and numerical simulation pose research challenges.This study focuses on the ice load of a cylinder structure breaking upward through the ice sheet form underneath in the Small Ice Model Basin of China Ship Scientific Research Center(CSSRC SIMB).A high-speed camera system was employed to observe the ice sheet failure during the tests,in which,with the loading position as center,local radial cracks and circumferential cracks were generated.A load sensor was used to measure the overall ice load during this process.Meanwhile,a numerical model was developed using LS-DYNA for validation and comparison.With this model,numerical simulation was conducted under various ice thicknesses and upgoing speeds to analyze the instantaneous curves of ice load.The calculation results were statistically analyzed under different working conditions to determine the influence of the factors on the ice load of the cylinder.The study explores the measurement method about ice load of objects vertically breaking through model ice sheet and is expected to provide some fundamental insights into the safety design of underwater structures operating in ice waters.展开更多
Cardiac arrest(CA)is a critical condition in the field of cardiovascular medicine.Despite successful resuscitation,patients continue to have a high mortality rate,largely due to post CA syndrome(PCAS).However,the inju...Cardiac arrest(CA)is a critical condition in the field of cardiovascular medicine.Despite successful resuscitation,patients continue to have a high mortality rate,largely due to post CA syndrome(PCAS).However,the injury and pathophysiological mechanisms underlying PCAS remain unclear.Experimental animal models are valuable tools for exploring the etiology,pathogenesis,and potential interventions for CA and PCAS.Current CA animal models include electrical induction of ventricular fibrillation(VF),myocardial infarction,high potassium,asphyxia,and hemorrhagic shock.Although these models do not fully replicate the complexity of clinical CA,the mechanistic insights they provide remain highly relevant,including post-CA brain injury(PCABI),post-CA myocardial dysfunction(PAMD),systemic ischaemia/reperfusion injury(IRI),and the persistent precipitating pathology.Summarizing the methods of establishing CA models,the challenges encountered in the modeling process,and the mechanisms of PCAS can provide a foundation for developing standardized CA modeling protocols.展开更多
To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints ...To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.展开更多
The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by sub...The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.展开更多
基金National Research,Development and Innovation Fund of the Ministry of Culture and Innovation under the National Laboratories Program(National Tumor Biology Laboratory,Grant/Award Number:2022-2.1.1-NL-2022-00010Senior Research Fellowship from National Health and Medical Research Council of Australia,Grant/Award Number:1156693+1 种基金Hungarian Thematic Excellence Program,Grant/Award Number:TKP2021-EGA-44Tour de Cure,Pioneering Grant,Grant/Award Number:RSP-253-18/19。
文摘Realistic models for cancer research representing disease progression that commensurately respond to therapeutics consistent with clinical observation are the holy grail for pre-clinical research and screening.Although such an ideal is elusive,well-characterized in vivo models facilitate our understanding of disease,progression,and therapeutic opportunities.Here,we characterize a commonly used syngeneic BALB/c mouse model of triple negative breast cancer(4T1)after establishing tumors in their flanks.Tumors developed at the subcutaneous injection site for all experimental mice and their volumes were monitored.We quantified a rare subset of breast cancer stemlike cells(CSCs),classified as CD44^(+)/CD24^(−)phenotypes in in vitro and ex vivo cell populations.Chromosome numbers in ex vivo metaphase cells were greater than cells cultured in vitro(89.4±3.4,range of 70-132 and 82.6±1.1,range of 70-128;respectively).Further,we observed different types of chromosome aberrations,including gap,deletion,exchange,interstitial deletion,terminal deletion,ring,dicentric,and Robertsonian translocations.For both sources of cells,the number of aberrations was dominated by deletions,terminal deletions,and Robertsonian translocations.Ex vivo cells exhibited greater prevalence of deletions and terminal deletions,whereas in vitro cells displayed more ring aberrations and Robertsonian translocations.In conclusion,we successfully characterized cancer cells from a syngeneic mouse model of breast cancer in terms of rare CSC proportion and a variety of chromosomal aberrations,which is useful for understanding tumor traits associated with cancer development and therapeutic action.The data act as a valuable resource for other studies using the 4T1 BALB/c model.
基金supported by the China Postdoctoral Science Foundation(CPSF)(Grant No.2024M762769)the Natural Science Basic Research Program of Shaanxi(Grant No.2024JC-YBQN-0333)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20232230).
文摘Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and finite element analyses.Three tests of the slope deformation under earthquake and post-rainfall earthquakes are first studied using image analysis techniques.Then,based on an elastoplastic constitutive model,numerical simulations are carried out using the finite element method and compared with the centrifuge test results.Finally,a parametric study is performed to clarify the effects of antecedent rainfall on earthquake-induced slope deformation.The results show that slope deformation caused by post-rainfall earthquakes differs from that caused by earthquakes without antecedent rainfall.The seepage flow and soil strength of the slope are affected by previous rainfall conditions,such as intensity and duration,which directly influence the slope deformation caused by the subsequent earthquake.Soil displacement and strain become greater and the slip surface is more noticeable during the post-rainfall earthquake of higher intensity.In addition,the time interval between the rainfall and the earthquake has a considerable impact on the detailed characteristics of the slope deformation,and the significant deformation occurs at the time of lowest soil strength when seepage flow reaches the lower part of the slope.Moreover,the repeated intermittent rainfall greatly affects the subsequent earthquake-induced slope deformation,the main characteristics of which are closely related to the changes in saturation and strength of the slope.However,with the prolonged time gap between each round of rainfall,the earthquake-induced slope deformation becomes insignificant.
基金funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.:5199990914048).
文摘Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.
基金supported by 2023 Higher Education Scientific Research Planning Project of China Society of Higher Education(No.23PG0408)2023 Philosophy and Social Science Research Programs in Jiangsu Province(No.2023SJSZ0993)+2 种基金Nantong Science and Technology Project(No.JC2023070)Key Project of Jiangsu Province Education Science 14th Five-Year Plan(Grant No.B-b/2024/02/41)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202407).
文摘Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.
基金supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
文摘BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
基金supported by the Central Government Guiding Local Science and Technology Development Fund Project(No.2024SZY0343)the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin(No.2022-YRUC-01-050205)+2 种基金the Higher Education Scientific Research Project of Inner Mongolia Autonomous Region(No.NJZZ23078)the project of Inner Mongolia"Prairie Talents"Engineering Innovation Entrepreneurship Talent Team,the Major Projects of Erdos Science and Technology(No.2022EEDSKJZDZX015)the Innovation Team of the Inner Mongolia Academy of Science and Technology(No.CXTD2023-01-016).
文摘Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.
基金National Natural Science Foundations of China(52275295)Central Plains Science and Technology Innovation Leading Talents(234200510026).
文摘Ion-exchange Polymer-Metal Composites(IPMCs)gain huge attentions due to large deformation,rapid electromechanical response,and high energy conversion efficiency.Deflection of IPMC arises from the volumetric swelling effect induced by the concentration gradient of hydrated cations between the two electrodes,thus the volume of hydrated cation deter-mines the motion magnitude and direction of IPMC.H ion is one of the most commonly used driving cations for IPMC.However,due to its unique characteristics,particularly the inability to accurately quantify its hydration volume,existing literatures primarily focus on the physical driving models for metallic cations,i.e.,Na+,no driving model for the H ion is reported until now.This paper proposes a novel model of H ion escape from the water's body-centered cubic lattice to count the hydration volume.Number(n)of water molecules carried by the H ion is solved by combining the Lennard-Jones potential energy function with Maxwell's velocity distribution.The specific n value is equivalent to 4.04 for the H ion inside Nafion electrolyte under a 3.0 V DC electric field.Substituting it into the classic Friction Model(proposed by Tadokoro et al.at 2000),actuation behaviors of H ion driven IPMC were therefore achieved through Matlab calculations and Abaqus simulations.The calculated results of dynamic displacement and force highly match to the experimental data form the Nafion IPMC actuator driven by same electric field,showing a highly reliability of the established escape model.
基金the Chinese Academy of Sciences Pioneer Hundred Talents Program and the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0708)supported by a MEXT(Ministry of Education,Culture,Sports,Science and Technology)KAKENHI(Grants-in-Aid for Scientific Research)grant(Grant No.21H05203)Kobe University Strategic International Collaborative Research Grant(Type B Fostering Joint Research).
文摘The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir uplift.Seismic imaging revealed that the upper slab was scraped and that the lower slab had subducted to a depth of>150 km.These features constitute the tectonic complexity of the Pamirs,as well as the thermal subduction mechanism involved,which remains poorly understood.Hence,in this study,high-resolution three-dimensional(3D)kinematic modeling is applied to investigate the thermal structure and geometry of the subducting slab beneath the Pamirs.The modeled slab configuration reveals distinct along-strike variations,with a steeply dipping slab beneath the southern Pamirs,a more gently inclined slab beneath the northern Pamirs,and apparent upper slab termination at shallow depths beneath the Pamirs.The thermal field reveals a cold slab core after delamination,with temperatures ranging from 400℃to 800℃,enveloped by a hotter mantle reaching~1400℃.The occurrence of intermediate-depth earthquakes aligns primarily with colder slab regions,particularly near the slab tear-off below the southwestern Pamirs,indicating a strong correlation between slab temperature and seismicity.In contrast,the northern Pamirs exhibit reduced seismicity at depth,which is likely associated with thermal weakening and delamination.The central Pamirs show a significant thermal anomaly caused by a concave slab,where the coldest crust does not descend deeply,further suggesting crustal detachment or mechanical failure.The lateral asymmetry in slab temperature possibly explains the mechanism of lateral tearing and differential slab-mantle coupling.
文摘In the lush heart of Uganda’s Busoga sub-region,Isaac Imaka is charting a new course for rural development.After seven years in national media,he left the newsroom and stepped into the soil.The former reporter with the Daily Monitor was driven by the belief that communities like his in Jinja North deserved more than chronic poverty and hand-to-mouth survival.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
文摘Fluid flow through fractured rock masses is a key process controlling the safety and performance of deep geoengineering systems,shaped by the complex interactions of thermal,hydraulic,mechanical and chemical(THMC)fields.This paper presents a systematic review of this subject with special emphasis on the multi-physics governing it.First,we elucidate the interdependent mechanisms and governing equations,highlighting the nonlinear,path-dependent,and evolving nature of the relationship between stress and permeability.Next,mainstream modeling approaches,including equivalent continuum,discrete fracture network(DFN),and dual-porosity/dual-permeability methods,are critically evaluated,and a strategy for model selection based on project scale and geological context is proposed accordingly.Moreover,experimental insights from single-fracture and triaxial flow studies are synthesized,revealing how effective stress,shear displacement,and fracture roughness control permeability evolution.In particular,the practical significance of THMC coupling is demonstrated through case studies on nuclear waste disposal,Enhanced Geothermal Systems,and tunneling projects.The reviewfurther explores AI-and machine learning-driven innovations,particularly physics-informed neural networks and hybrid modeling,which address limitations in computational efficiency,data scarcity,and physical consistency.Finally,persistent challenges,including multi-scale coupling,parameter uncertainty,and complex fracture network representation are identified and critically discussed while paying attention to future developments.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
文摘Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visited our hospital from January 2024 to December 2024 were selected as samples and randomly divided into groups.The observation group received rehabilitation nursing based on the mind mapping model combined with psychological intervention,while the control group received routine intervention.The differences in emotional scores,self-care ability scores,compliance,and complications were compared between the two groups.Results:The anxiety(SAS)and depression(SDS)scores of the observation group were lower than those of the control group,while the self-care ability scale(ESCA)score was higher than that of the control group(P<0.05).The compliance rate of the observation group was higher than that of the control group(P<0.05).The complication rate of NS in the observation group was lower than that in the control group(P<0.05).Conclusion:Rehabilitation nursing based on the mind mapping model combined with psychological intervention can enhance self-care ability,reduce negative emotions,and reduce complications in NS nursing,which is efficient and feasible.
基金Deputy for Research and Technology,Kermanshah University of Medical Sciences,Grant/Award Number:4030031。
文摘Background:Due to the widespread use of cell phone devices today,numerous re-search studies have focused on the adverse effects of electromagnetic radiation on human neuropsychological and reproductive systems.In most studies,oxidative stress has been identified as the primary pathophysiological mechanism underlying the harmful effects of electromagnetic waves.This paper aims to provide a holistic review of the protective effects of melatonin against cell phone-induced electromag-netic waves on various organs.Methods:This study is a systematic review of articles chosen by searching Google Scholar,PubMed,Embase,Scopus,Web of Science,and Science Direct using the key-words‘melatonin’,‘cell phone radiation’,and‘animal model’.The search focused on articles written in English,which were reviewed and evaluated.The PRISMA process was used to review the articles chosen for the study,and the JBI checklist was used to check the quality of the reviewed articles.Results:In the final review of 11 valid quality-checked articles,the effects of me-latonin in the intervention group,the effects of electromagnetic waves in the case group,and the amount of melatonin in the chosen organ,i.e.brain,skin,eyes,testis and the kidney were thoroughly examined.The review showed that electromagnetic waves increase cellular anti-oxidative activity in different tissues such as the brain,the skin,the eyes,the testis,and the kidneys.Melatonin can considerably augment the anti-oxidative system of cells and protect tissues;these measurements were sig-nificantly increased in control groups.Electromagnetic waves can induce tissue atro-phy and cell death in various organs including the brain and the skin and this effect was highly decreased by melatonin.Conclusion:Our review confirms that melatonin effectively protects the organs of an-imal models against electromagnetic waves.In light of this conclusion and the current world-wide use of melatonin,future studies should advance to the stages of human clinical trials.We also recommend that more research in the field of melatonin physi-ology is conducted in order to protect exposed cells from dying and that melatonin should be considered as a pharmaceutical option for treating the complications result-ing from electromagnetic waves in humans.
基金supported by the National Natural Science Foundation of China(Grant No.42274003)PWL was supported by Swarm DISC(Swarm Data,Innovation,and Science Cluster)+2 种基金funded by the European Space Agency(ESAContract No.4000109587)HFR acknowledges funding from the UK Natural Environment Research Council(Grant No.NE/V010867/1)。
文摘Measurements from geomagnetic satellites continue to underpin advances in geomagnetic field models that describe Earth's internally generated magnetic field.Here,we present a new field model,MSCM,that integrates vector and scalar data from the Swarm,China Seismo-Electromagnetic Satellite(CSES),and Macao Science Satellite-1(MSS-1)missions.The model spans from 2014.0 to 2024.5,incorporating the core,lithospheric,and magnetospheric fields,and it shows characteristics similar to other published models based on different data.For the first time,we demonstrate that it is possible to successfully construct a geomagnetic field model that incorporates CSES vector data,albeit one in which the radial and azimuthal CSES vector components are Huber downweighted.We further show that data from the MSS-1 can be integrated within an explicitly smoothed,fully time-dependent model description.Using the MSCM,we identify new behavior of the South Atlantic Anomaly,the broad region of low magnetic field intensity over the southern Atlantic.This prominent feature appears split into a western part and an eastern part,each with its own intensity minimum.Since 2015,the principal western minimum has undergone only modest intensity decreases of 290 nT and westward motion of 20 km per year,whereas the recently formed eastern minimum has shown a 2–3 times greater intensity drop of 730 nT with no apparent east-west motion.
文摘Ice load on underwater vehicles breaking through ice covers from underneath is a significant concern for researchers in polar exploration,and the research on this problem is still in its early stages.Both mechanical experimental measurement and numerical simulation pose research challenges.This study focuses on the ice load of a cylinder structure breaking upward through the ice sheet form underneath in the Small Ice Model Basin of China Ship Scientific Research Center(CSSRC SIMB).A high-speed camera system was employed to observe the ice sheet failure during the tests,in which,with the loading position as center,local radial cracks and circumferential cracks were generated.A load sensor was used to measure the overall ice load during this process.Meanwhile,a numerical model was developed using LS-DYNA for validation and comparison.With this model,numerical simulation was conducted under various ice thicknesses and upgoing speeds to analyze the instantaneous curves of ice load.The calculation results were statistically analyzed under different working conditions to determine the influence of the factors on the ice load of the cylinder.The study explores the measurement method about ice load of objects vertically breaking through model ice sheet and is expected to provide some fundamental insights into the safety design of underwater structures operating in ice waters.
基金supported by the National Key Research and Development Program(2021YFC3002205)the Postgraduate Research and Innovation Program of Tianjin Municipal Education Commission(2022BKY113),China.
文摘Cardiac arrest(CA)is a critical condition in the field of cardiovascular medicine.Despite successful resuscitation,patients continue to have a high mortality rate,largely due to post CA syndrome(PCAS).However,the injury and pathophysiological mechanisms underlying PCAS remain unclear.Experimental animal models are valuable tools for exploring the etiology,pathogenesis,and potential interventions for CA and PCAS.Current CA animal models include electrical induction of ventricular fibrillation(VF),myocardial infarction,high potassium,asphyxia,and hemorrhagic shock.Although these models do not fully replicate the complexity of clinical CA,the mechanistic insights they provide remain highly relevant,including post-CA brain injury(PCABI),post-CA myocardial dysfunction(PAMD),systemic ischaemia/reperfusion injury(IRI),and the persistent precipitating pathology.Summarizing the methods of establishing CA models,the challenges encountered in the modeling process,and the mechanisms of PCAS can provide a foundation for developing standardized CA modeling protocols.
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)。
文摘To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.
文摘The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.
